The focus on of this work is on SLAM in unknown environment by recognizing objects and utilizing their positions (object SLAM).
A factor graph is the natural representation, as objects can be easily represented as landmarks.
A map represented by objects is desirable, as objects are very rich in semantic meanings.
By using objects, robots can interact with other agents and perform tasks at semantic level, such as searching for people in a forest, grasping objects, and detecting moving cars on streets.
Object SLAM requires the robot to be able to detect objects, generate measurements, and associate these measurements to unique identifiers.
In this work, object detection refers to the problem of identifying the occurrence of objects of some predefined object classes within an image.
An object measurement is a 3D location of the detected object with respect to the robot pose.
Data association refers to the problem of associating object measurements to unique identifiers across images.
The problem of object detection has been an important topic in the computer vision community.
Deep learning approaches have achieved significant success on object detections within individual images [1]}, [2]}, [3]}, [4]}, [5]}.
These approaches also have the ability to generalize: once a detector is trained to recognize a object class, such as chairs, the detector can detect different instances of the same class even in different shape, color, and background settings.
Some recent work on Region-based Convolutional Neural Networks [2]}, [7]} gained significant success on training deep learning models to detect multiple objects instances within a single image.
However, object detections only suggest the existence of objects of certain predefined object classes in an image, but provide no data association between images.
given that an object of a certain class is detected in two images, the object detector provides no information on whether or not the detected objects in the two images are the same object.
This is problematic for SLAM especially when there are multiple objects of the same object class in an environment.
How reliably SLAM can be achieved using only these ambiguous object detections remains an open question.
As illustrated in fig:officefloormap, there are multiple instances of the same object class, such as chairs. The robot would need to establish the data association of object detections across images from different views. Note that data association and SLAM are inherently coupled problems: good data association guarantees the convergence of SLAM algorithms, and good SLAM solution gives good initialization of data association.
